More works [1]}, [2]}, [3]}, [4]}, [5]}, [6]} pay attention to the entropy network and obtain obvious improvements. Ballé et al. [1]} use a fully factorized prior to minimize the entropy of the elements of the whole latent representation. An improved version [2]} is proposed to use a hierarchical learned prior network to occupy the fact that spatially neighboring elements of the latent representation tend to vary together in their scales. Furthermore, context-adaptive based models [3]}, [4]}, [5]}, [12]} are introduced to utilize a neural network like PixelCNN [13]} to incorporate predictions from neighboring symbols, avoiding storing additional bits. This kind of method outperforms the top standard image codec (BPG) on both the PSNR and MS-SSIM distortion metrics.
After that, Qian [6]} builds up a global relevance throughout latent features to further explore the relationship in the whole picture. All these works show that designing an accurate entropy model is crucial or even the only thing for learned image compression.
